Inroduction



The rapid expansion of short-term rental platforms like Airbnb has fundamentally transformed urban housing markets and tourism economies. In Los Angeles County, this increase raises critical questions about whether Airbnb increases airbnb solely increasing access to tourism economy access or if it also reinforces existing patterns of racial and economic inequality. This report analyzes Airbnb distribution patterns across LA County, examining relationships between short-term rental activity and neighborhood socioeconomic characteristics including racial composition, median income, educational attainment, public transit access, and violent crime. Drawing on Inside Airbnb listing data (September 2025) and American Community Survey demographic indicators, the analysis employs spatial analysis techniques to investigate whether Airbnb concentrates in privileged neighborhoods, potentially exacerbating displacement pressures documented by Wachsmuth and Weisler (2018) in their rent gap framework. The findings reveal that racial composition and income, rather than crime or transit access, primarily correlate with where Airbnb activity concentrates and how it is priced, with implications for equitable urban development policy.



Part A: Data and Spatial Patterns



The primary dataset comprises 45,886 Airbnb listings for Los Angeles County obtained from Inside Airbnb (insideairbnb.com), scraped in September 2025. Variables include price, coordinates, room type, availability, and review metrics. Strengths include comprehensive geographic coverage and precise geolocation enabling spatial analysis. However, limitations include single snapshot temporal coverage preventing longitudinal analysis, potential inactive or fraudulent listings in user filled (crowd-sourced) data, and exclusion of unlisted short term rentals operating outside Airbnb’s platform.

I applied three filters to focus on active listings: removed entries with missing or zero prices (likely delisted properties), filtered listings above the 99th percentile ($2,573, retaining 99% while excluding extremes reaching $85,000), and removed listings with zero availability lacking reviews since 2024 (likely inactive). The final dataset comprises 36,362 active listings.

Socioeconomic data derives from the ACS 2019-2023 5-year estimates (2,498 LA County census tracts), accessed via tidycensus and provided files. Variables include racial composition (B03002), median income (B19013_001E), poverty (B17001), education (B23006), and commuting patterns (B08006). Strengths include the fact that it is government data with established methodology and granular data on census tract geography. Limitations include 5-year aggregation obscuring recent changes, margins of error in small populations, and temporal mismatch with the 2025 Airbnb data.

All spatial data were transformed to EPSG:26911 (NAD83/UTM Zone 11N), appropriate for LA County’s longitude range within UTM Zone 11. The original census boundaries (EPSG:4269, unprojected) required transformation for accurate distance calculations, particularly the 200 meter buffer analysis in Question 5. UTM Zone 11N provides minimal regional distortion with metric units facilitating spatial operations.

Airbnb Distribution and Pricing Patterns



Figure 1 maps listings by room type. Entire homes demonstrate markedly higher density, clustering around centre, centre south, and centre west areas, with dense coastal concentrations (Santa Monica, Venice, Malibu) identified as Airbnb hotspots in prior research (Sarkar, Koohikamali and Pick, 2017). Centre south (downtown LA) shows substantial presence of both entire homes and other room types, indicating the likely presence of both tourist and short duration business travellers. Southern and eastern portions display sparse presence across all types, revealing geographic inequalities in working class neighborhoods in southeast of LA (Sarkar, Koohikamali and Pick, 2017).

Semi transparent symbology (alpha equals 0.2) creates natural density encoding where darker concentrations indicate higher volumes, while thin borders (0.3pt) follow cartographic best practices for dense city scale point data.

Figure 2 employs Jenks natural breaks classification, selected to reveal genuine price discontinuities by maximizing within class homogeneity. An embedded histogram illustrates the skewed price distribution (most listings below $500), justifying Jenks over equal intervals. Highest prices ($772 to $1,357) concentrate in centre west coastal communities, while lowest prices ($18 to $147) dominate northern and southern inland areas. Gray areas indicate census tracts without Airbnb listings, representing 9% of LA County (227 tracts). Of these, 167 tracts genuinely lack Airbnb presence, predominantly in southeastern LA and northern periphery, revealing either systematic geographic exclusion from tourism economy participation, or underreporting in those areas. An additional 45 tracts are uninhabited areas with zero or very low populations which could potentially constitute military installations or national forest areas where demographic data might not be as well documented. The remaining 60 tracts had Airbnbs in the original dataset but all listings were removed through outlier filtering, indicating these areas contained only extreme priced properties. I chose to display missing data using gray shading rather than omitting these tracts because the spatial pattern of exclusion is itself analytically significant, revealing which communities lack access to short term rental economic opportunities. The Yellow Orange Red scheme provides intuitive low to high encoding accessible to colorblind readers through luminance variation.

Figure 1




Figure 2




Socioeconomic Geography



I calculated percentage non-Hispanic white population (B03002_003E/B03002_001E×100) via tidycensus, standardizing by census tract population to ensure comparability across varying tract sizes. Median household income came from provided ACS data (B19013_001E). These variables were selected because gentrification research demonstrates Airbnb concentrates in neighborhoods with particular racial-economic compositions (Wachsmuth & Weisler, 2018; Zhang and J.C. Fu, 2022).

Figure 3 employs quantile classification, selected over Jenks because while Jenks revealed extreme inequality (bottom category spanning $7,000 to $66,416), quantile ensures balanced visual representation with each quintile representing 20% of tracts. Higher white percentages (over 50%) concentrate in northern and centre west coastal areas, while southern areas show predominantly lower percentages (below 11%). Income patterns substantially overlap: centre west coastal and northern areas display highest incomes ($120,000 to $250,000), while centre south falls into the lowest bracket ($7,000 to $60,000), coinciding with lowest white percentages. White or gray areas in both maps indicate 45 census tracts with missing ACS data, of which 21 have zero population and 29 have fewer than 100 residents. These might represent uninhabited zones such as National Forest, military bases or industrial areas without census records due to privacy concerns or lack of residential population. Displaying these missing data areas as distinct gray shading reveals the geographic extent of unpopulated zones and prevents misleading interpolation across uninhabited territory. This spatial correlation potentially reflects LA’s residential segregation history and the fact that income is positively correlated to race even today. The use of divergent color schemes (Purples for race, Greens for income) aim to distinguish variables while maintaining clarity.

Figure 3


Figure 4’s bivariate choropleth combines ln(price) and percentage white using quantile classification (3×3 grid), which normalizes both variables. Natural log transformation compresses the $18 to $2,573 range into an interpretable scale emphasizing proportional differences. Centre north and centre west coastal areas display dark colors (high on both dimensions), while southern tracts show light colors (low on both). Areas with high prices but low white percentages are present in pockets of the north and extremely few regions in the south. However, given the very few observations in the north, this might be spurious correlations, suggesting that there is a positive correlation between airbnb price and percentage white.

This pattern reflects interconnected explanations. First, non White hosts charge 7.58% less than White hosts in LA (Jaeger and Sleegers, 2022), so predominantly white neighborhoods with more White hosts may command higher prices through racial pricing disparities. Second, building on Figure 3’s demonstrated race income correlation (r equals 0.601), expensive coastal Airbnbs might reflect both racial composition and neighborhood wealth, amenities, and tourist infrastructure. This dual mechanism aligns with findings that discrimination persists across diverse and homogeneous neighborhoods (Edelman, Luca and Svirsky, 2015), indicating host race and neighborhood characteristics independently influence pricing.

Figure 4





Transit Infrastructure and Proximity



I queried 369 Metro/light rail stations via OSM, capturing LA’s Red Line subway and Blue, Green, Gold, Expo, Purple light rail lines (Anderson, 2014). Figure 5’s kernel density heatmap (bandwidth=15km) reveals highest station density in centre-south downtown, declining concentrically outward through high, medium, and low density rings. This pattern reflects converging lines creating downtown’s dense node, while vast areas on the peripheries and the coast have minimal rail coverage, reflecting LA’s polycentric structure where proximity to the city center does not significantly affect Airbnb supply, especially given the high supply on the centre-west portion which has seemingly low transport density (Zhang and J.C. Fu, 2022).

Figure 6 examines Downtown LA, where 225 of 861 listings (26%) fall within 200 meter buffers of transit stations, with remaining listings clustering near transit corridors. This demonstrates transit accessibility correlates with short term rental locations in tourist heavy mixed use areas. However, the modest 26% proximity rate suggests other factors also shape distribution. This contrasts with denser cities where short term renters show flatter distance gradients from city centers (Coles et al., 2017), possibly because LA’s automobile culture reduces transit’s relative importance for tourist accommodations.

Figure 5


Figure 6




Part B: Statistical Relationships



I employed scatterplot analysis examining average Airbnb prices against four socioeconomic variables across LA neighborhoods, quantifying the spatial relationships observed in Figures 2-4. Aggregating to neighborhood level facilitates comparison with literature typically analyzing neighborhoods as units (Coles et al., 2017).

Figure 7 reveals positive correlations between prices and both percentage white (r=0.602) and median income (r=0.636), indicating premiums in wealthier, predominantly white neighborhoods. Similar correlation strengths reflect substantial race-class overlap (r=0.60 in correlation matrix). Santa Monica (on the centre west coast) exemplifies this pattern with high values on both dimensions and elevated prices (~$350), while Downtown shows lower white percentage (30%) with moderate prices ($250). Hollywood Hills West emerges as an outlier with very high prices despite moderate white percentages, likely reflecting celebrity cachet and luxury housing rather than demographics alone.

The negative transit correlation (r=-0.27) reveals that surprisingly, dense transport networks do not create an airbnb premium and are in fact negatively associated with it. This might be because of two factors : a) that there are so many airbnbs present in areas of high density transit (centre south) that it proves a challenge to charge premium prices, or b) since public transport is not as widely used in certain airbnb dense regions in LA, such as centre-west regions like Santa Monica, which are well to do and more car-dependent with lower transport network density thus leading to a negative correlation. This could potentially evidence the fact that LA’s transit infrastructure disproportionately serves tourism rather than access to mobility-dependent communities in the centre-south, although a weak correlation does not provide strong enough evidence to this fact. Education shows positive correlation (r=0.532), supporting findings that highly educated neighborhoods were initial hotspots (Coles et al., 2017), though weaker than race or income as predictors.

Visualization employed scatterplots with linear trend lines, highlighting three key neighborhoods (Santa Monica, Downtown, Hollywood Hills West) as labeled points to connect abstract correlations to concrete places analyzed throughout the report. The correlation matrix reveals that race and education correlate very strongly (r equals 0.777), indicating educational attainment itself could be racialized in LA’s geography.


Figure 7



Part C: Crime and Airbnb Distribution

To examine whether crime shapes Airbnb distribution beyond demographics, I incorporated 13,607 violent crime incidents (homicide, rape, robbery, aggravated assault) from LAPD data (January 2024-September 2025). Recent empirical research establishes causal Airbnb-crime links, though findings vary. O’Brien and Heydari found Boston Airbnb density predicted violence increases after one year, because large population of unknown, temporary members begin to undermine neighborhood social organization (Ke, O’brien and Heydari, 2020). London evidence showed mixed results with positive correlations with property crime but negative correlations with violent crime (Maldonado-Guzmán, Francisco José Chamizo-Nieto and Reyes-Corredera, 2024).

Figure 8 presents side-by-side choropleths comparing crime distribution (continuous scale using square-root transformation to handle skew) with binary Airbnb density reflecting <50 vs. 50+ listings per tract, capturing dense tourist spots vs not so dense spots. Spatial analysis reveals partial overlap rather than systematic correlation. Highest Airbnb density concentrates in centre-west coastal areas with low crime, while violent crime concentrates in centre and centre-south areas where Airbnb density varies. Statistical analysis confirms weak relationships: prices correlate negatively with crime counts (r=-0.183) and rates (r=-0.081), indicating modest price penalties in higher-crime areas, while Airbnb counts show virtually no correlation with crime (r=0.019).

This weak crime Airbnb relationship contrasts sharply with stronger race (r equals 0.494) and income (r equals 0.504) correlations from Figure 7, suggesting demographic composition matters substantially more than crime for Airbnb distribution. The finding challenges simplistic crime deters tourists narratives. It also does not provide enough evidence to support arguments that an increase in Airbnb density could potentially increase crime through social disorganization, although testing this accurately would require longitudinal data unavailable in this cross sectional analysis. The pattern also suggests centre south areas experiencing both crime and Airbnb reflect confounding urban centrality where busy city centers naturally have both tourism and crime independent of causal relationships (Maldonado-Guzmán, Francisco José Chamizo-Nieto and Reyes-Corredera, 2024). The binary classification for Airbnb (50 plus threshold) was selected based on distribution analysis showing 95% of tracts have fewer than 50 listings, with over half having 10 or fewer, making 50 plus a meaningful high density designation distinguishing genuine tourist concentration zones from areas with sparse or moderate short term rental presence.

Figure 8


Conclusion

This spatial analysis of Los Angeles County reveals that Airbnb distribution patterns might systematically reinforce rather than disrupt existing urban inequalities. Short term rentals concentrate in wealthy, predominantly white coastal neighborhoods (Santa Monica, Malibu, Westside), commanding premium prices while remaining sparse or absent in lower income communities of color (South LA, East LA). The moderate to strong correlations between Airbnb prices and both racial composition (r equals 0.494) and median income (r equals 0.504) demonstrate that demographic factors could be large determiners of where tourism economy benefits flow.

Three key findings emerge. First, the race income Airbnb nexus operates consistently across LA’s geography, with rare exceptions like Hollywood Hills West. Second, transit infrastructure, while not so prevalent across the whole county, tends to serve areas of high airbnb density and low prices, potentially aiming to serve tourists rather than transit dependent communities, as evidenced by negative correlation between Airbnb prices and resident transit use (r equals negative 0.175). However, the weak correlation suggests weak evidence and it could also be that the rest of LA is a lot more car-dependent leading to lower public transport density. Third, crime shows weak influence on Airbnb distribution (r equals negative 0.18), suggesting safety concerns matter less than other potential factors like race and perceptions of neighborhood desirability as well as centrality to the city.

For LA decision makers, these patterns indicate that market driven short term rental activity concentrates high priced airbnbs in already privileged neighborhoods, while they flood lower income neighbourhoods with a higher number of cheaper rentals. The strong demographic correlations compared to weak crime and transit effects suggest that policies addressing Airbnb’s inequitable impacts must directly target structural racial and economic inequalities through affordable housing protections, community benefit agreements, and support for local entrepreneurship in historically marginalized communities, rather than assuming infrastructure investments or crime reduction alone will democratize tourism access. While these figures reflect correlation and not causation, the analysis demonstrates how short term rental platforms intersect with and potentially amplify long standing patterns of residential segregation and uneven development in Los Angeles.

REFERENCES



Coles, P.A., Egesdal, M., Ellen, I.G., Li, X. and Sundararajan, A. (2017). Airbnb Usage Across New York City Neighborhoods: Geographic Patterns and Regulatory Implications. SSRN Electronic Journal. doi:https://doi.org/10.2139/ssrn.3048397.

Edelman, B.G., Luca, M. and Svirsky, D. (2015). Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment. SSRN Electronic Journal. doi:https://doi.org/10.2139/ssrn.2701902.

Jaeger, B. and Sleegers, W.W.A. (2022). Racial disparities in the sharing economy: Evidence from more than 100,000 Airbnb hosts across 14 countries. Journal of the Association for Consumer Research, 8(1). doi:https://doi.org/10.1086/722700.

Ke, L., O’brien, D. and Heydari, B. (2020). Airbnb and Neighborhood Crime: The Incursion of Tourists or the Erosion of Local Social Dynamics? SSRN Electronic Journal. doi:https://doi.org/10.2139/ssrn.3725823.

Maldonado-Guzmán, D.J., Francisco José Chamizo-Nieto and Reyes-Corredera, S. (2024). Home sharing or crime sharing? Evidences of the relationship between Airbnb, crime and structural factors in Malaga, Spain. GIScience & Remote Sensing, 61(1). doi:https://doi.org/10.1080/15481603.2024.2384330.

Sarkar, A., Koohikamali, M. and Pick, J.B. (2017). SPATIOTEMPORAL PATTERNS AND SOCIOECONOMIC DIMENSIONS OF SHARED ACCOMMODATIONS: THE CASE OF AIRBNB IN LOS ANGELES, CALIFORNIA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-4/W2, pp.107–114. doi:https://doi.org/10.5194/isprs-annals-iv-4-w2-107-2017.

Wachsmuth, D. and Weisler, A. (2018). Airbnb and the rent gap: Gentrification through the sharing economy. Environment and Planning A: Economy and Space, 50(6), pp.1147–1170. Zhang, Z. and J.C. Fu, R. (2022). The spatial distribution of Airbnb supply in Los Angeles. Tourism Analysis. doi:https://doi.org/10.3727/108354222x16571659728565.